Preparing for DP-100 Designing and Implementing a Data Science Solution on Azure Certificate exam? Don’t know where to start? This post is the DP-100 Certificate Study Guide (with links to each exam objective).
I have curated a list of articles from Microsoft documentation for each objective of DP-100 exam. I hope this article will help you to prepare for DP-100 Certification exam. Also, please share the post within your circles so it helps them to prepare for the exam.
DP-100 Course (Online Training)
DP-100 Practice Tests & Labs
Udemy Practice Test DP-100 Practice Exam Questions Labs on GitHub DP-100 Implementing an Azure Data Science Solution
DP-100 E-book (PDF)
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Define and prepare the development environment (15-20%)
Select development environment
Assess the deployment environment constraints
Analyze and recommend tools that meet system requirements
Select the development environment
Set up development environment
Create an Azure data science environment
Configure data science work environments
Quantify the business problem
Define technical success metrics
Prepare data for modeling (25-30%)
Transform data into usable datasets
Develop data structures
Design a data sampling strategy
Design the data preparation flow
Perform Exploratory Data Analysis (EDA)
Review visual analytics data to discover patterns and determine next steps
Identify anomalies, outliers, and other data inconsistencies
Create descriptive statistics for a dataset
Cleanse and transform data
Resolve anomalies, outliers, and other data inconsistencies
Standardize data formats
Set the granularity for data
Perform feature engineering (15-20%)
Perform feature extraction
Perform feature extraction algorithms on numerical data
Perform feature extraction algorithms on non-numerical data
Perform feature selection
Define the optimality criteria
Apply feature selection algorithms
Develop models (40-45%)
Select an algorithmic approach
Determine appropriate performance metrics
Implement appropriate algorithms
Consider data preparation steps that are specific to the selected algorithms
Determine ideal split based on the nature of the data
Determine number of splits
Determine relative size of splits
Ensure splits are balanced
Identify data imbalances
Resample a dataset to impose balance
Adjust performance metric to resolve imbalances
Train the model
Select early stopping criteria
Evaluate model performance
Score models against evaluation metrics
Identify and address overfitting
Identify root cause of performance results
This brings us to the end of DP-100 Study Guide
What do you think? Let me know in the comments section if I have missed out on anything. Also, I love to hear from you about how your preparation is going on!
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